{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hide\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Collaborative Filtering Deep Dive"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A First Look at the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.collab import *\n",
    "from fastai.tabular.all import *\n",
    "path = untar_data(URLs.ML_100k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>movie</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>881250949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>186</td>\n",
       "      <td>302</td>\n",
       "      <td>3</td>\n",
       "      <td>891717742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>377</td>\n",
       "      <td>1</td>\n",
       "      <td>878887116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>244</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>880606923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>166</td>\n",
       "      <td>346</td>\n",
       "      <td>1</td>\n",
       "      <td>886397596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user  movie  rating  timestamp\n",
       "0   196    242       3  881250949\n",
       "1   186    302       3  891717742\n",
       "2    22    377       1  878887116\n",
       "3   244     51       2  880606923\n",
       "4   166    346       1  886397596"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = pd.read_csv(path/'u.data', delimiter='\\t', header=None,\n",
    "                      names=['user','movie','rating','timestamp'])\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "last_skywalker = np.array([0.98,0.9,-0.9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "user1 = np.array([0.9,0.8,-0.6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.1420000000000003"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(user1*last_skywalker).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "casablanca = np.array([-0.99,-0.3,0.8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.611"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(user1*casablanca).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning the Latent Factors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the DataLoaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Get Shorty (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Copycat (1995)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie              title\n",
       "0      1   Toy Story (1995)\n",
       "1      2   GoldenEye (1995)\n",
       "2      3  Four Rooms (1995)\n",
       "3      4  Get Shorty (1995)\n",
       "4      5     Copycat (1995)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies = pd.read_csv(path/'u.item',  delimiter='|', encoding='latin-1',\n",
    "                     usecols=(0,1), names=('movie','title'), header=None)\n",
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>movie</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>881250949</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>875747190</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>226</td>\n",
       "      <td>242</td>\n",
       "      <td>5</td>\n",
       "      <td>883888671</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>154</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>879138235</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>306</td>\n",
       "      <td>242</td>\n",
       "      <td>5</td>\n",
       "      <td>876503793</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user  movie  rating  timestamp         title\n",
       "0   196    242       3  881250949  Kolya (1996)\n",
       "1    63    242       3  875747190  Kolya (1996)\n",
       "2   226    242       5  883888671  Kolya (1996)\n",
       "3   154    242       3  879138235  Kolya (1996)\n",
       "4   306    242       5  876503793  Kolya (1996)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = ratings.merge(movies)\n",
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>title</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>542</td>\n",
       "      <td>My Left Foot (1989)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>422</td>\n",
       "      <td>Event Horizon (1997)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>311</td>\n",
       "      <td>African Queen, The (1951)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>595</td>\n",
       "      <td>Face/Off (1997)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>617</td>\n",
       "      <td>Evil Dead II (1987)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>158</td>\n",
       "      <td>Jurassic Park (1993)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>836</td>\n",
       "      <td>Chasing Amy (1997)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>474</td>\n",
       "      <td>Emma (1996)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>466</td>\n",
       "      <td>Jackie Chan's First Strike (1996)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>554</td>\n",
       "      <td>Scream (1996)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dls = CollabDataLoaders.from_df(ratings, item_name='title', bs=64)\n",
    "dls.show_batch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'user': (#944) ['#na#',1,2,3,4,5,6,7,8,9...],\n",
       " 'title': (#1635) ['#na#',\"'Til There Was You (1997)\",'1-900 (1994)','101 Dalmatians (1996)','12 Angry Men (1957)','187 (1997)','2 Days in the Valley (1996)','20,000 Leagues Under the Sea (1954)','2001: A Space Odyssey (1968)','3 Ninjas: High Noon At Mega Mountain (1998)'...]}"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dls.classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_users  = len(dls.classes['user'])\n",
    "n_movies = len(dls.classes['title'])\n",
    "n_factors = 5\n",
    "\n",
    "user_factors = torch.randn(n_users, n_factors)\n",
    "movie_factors = torch.randn(n_movies, n_factors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_hot_3 = one_hot(3, n_users).float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([-0.4586, -0.9915, -0.4052, -0.3621, -0.5908])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_factors.t() @ one_hot_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([-0.4586, -0.9915, -0.4052, -0.3621, -0.5908])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_factors[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collaborative Filtering from Scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Example:\n",
    "    def __init__(self, a): self.a = a\n",
    "    def say(self,x): return f'Hello {self.a}, {x}.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello Sylvain, nice to meet you.'"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ex = Example('Sylvain')\n",
    "ex.say('nice to meet you')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DotProduct(Module):\n",
    "    def __init__(self, n_users, n_movies, n_factors):\n",
    "        self.user_factors = Embedding(n_users, n_factors)\n",
    "        self.movie_factors = Embedding(n_movies, n_factors)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        users = self.user_factors(x[:,0])\n",
    "        movies = self.movie_factors(x[:,1])\n",
    "        return (users * movies).sum(dim=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([64, 2])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x,y = dls.one_batch()\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = DotProduct(n_users, n_movies, 50)\n",
    "learn = Learner(dls, model, loss_func=MSELossFlat())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.993168</td>\n",
       "      <td>0.990168</td>\n",
       "      <td>00:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.884821</td>\n",
       "      <td>0.911269</td>\n",
       "      <td>00:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.671865</td>\n",
       "      <td>0.875679</td>\n",
       "      <td>00:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.471727</td>\n",
       "      <td>0.878200</td>\n",
       "      <td>00:11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.361314</td>\n",
       "      <td>0.884209</td>\n",
       "      <td>00:12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(5, 5e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DotProduct(Module):\n",
    "    def __init__(self, n_users, n_movies, n_factors, y_range=(0,5.5)):\n",
    "        self.user_factors = Embedding(n_users, n_factors)\n",
    "        self.movie_factors = Embedding(n_movies, n_factors)\n",
    "        self.y_range = y_range\n",
    "        \n",
    "    def forward(self, x):\n",
    "        users = self.user_factors(x[:,0])\n",
    "        movies = self.movie_factors(x[:,1])\n",
    "        return sigmoid_range((users * movies).sum(dim=1), *self.y_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.973745</td>\n",
       "      <td>0.993206</td>\n",
       "      <td>00:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.869132</td>\n",
       "      <td>0.914323</td>\n",
       "      <td>00:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.676553</td>\n",
       "      <td>0.870192</td>\n",
       "      <td>00:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.485377</td>\n",
       "      <td>0.873865</td>\n",
       "      <td>00:12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.377866</td>\n",
       "      <td>0.877610</td>\n",
       "      <td>00:11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = DotProduct(n_users, n_movies, 50)\n",
    "learn = Learner(dls, model, loss_func=MSELossFlat())\n",
    "learn.fit_one_cycle(5, 5e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DotProductBias(Module):\n",
    "    def __init__(self, n_users, n_movies, n_factors, y_range=(0,5.5)):\n",
    "        self.user_factors = Embedding(n_users, n_factors)\n",
    "        self.user_bias = Embedding(n_users, 1)\n",
    "        self.movie_factors = Embedding(n_movies, n_factors)\n",
    "        self.movie_bias = Embedding(n_movies, 1)\n",
    "        self.y_range = y_range\n",
    "        \n",
    "    def forward(self, x):\n",
    "        users = self.user_factors(x[:,0])\n",
    "        movies = self.movie_factors(x[:,1])\n",
    "        res = (users * movies).sum(dim=1, keepdim=True)\n",
    "        res += self.user_bias(x[:,0]) + self.movie_bias(x[:,1])\n",
    "        return sigmoid_range(res, *self.y_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.929161</td>\n",
       "      <td>0.936303</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.820444</td>\n",
       "      <td>0.861306</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.621612</td>\n",
       "      <td>0.865306</td>\n",
       "      <td>00:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.404648</td>\n",
       "      <td>0.886448</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.292948</td>\n",
       "      <td>0.892580</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = DotProductBias(n_users, n_movies, 50)\n",
    "learn = Learner(dls, model, loss_func=MSELossFlat())\n",
    "learn.fit_one_cycle(5, 5e-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weight Decay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-2,2,100)\n",
    "a_s = [1,2,5,10,50] \n",
    "ys = [a * x**2 for a in a_s]\n",
    "_,ax = plt.subplots(figsize=(8,6))\n",
    "for a,y in zip(a_s,ys): ax.plot(x,y, label=f'a={a}')\n",
    "ax.set_ylim([0,5])\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.972090</td>\n",
       "      <td>0.962366</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.875591</td>\n",
       "      <td>0.885106</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.723798</td>\n",
       "      <td>0.839880</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.586002</td>\n",
       "      <td>0.823225</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.490980</td>\n",
       "      <td>0.823060</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = DotProductBias(n_users, n_movies, 50)\n",
    "learn = Learner(dls, model, loss_func=MSELossFlat())\n",
    "learn.fit_one_cycle(5, 5e-3, wd=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Our Own Embedding Module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(#0) []"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class T(Module):\n",
    "    def __init__(self): self.a = torch.ones(3)\n",
    "\n",
    "L(T().parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(#1) [Parameter containing:\n",
       "tensor([1., 1., 1.], requires_grad=True)]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class T(Module):\n",
    "    def __init__(self): self.a = nn.Parameter(torch.ones(3))\n",
    "\n",
    "L(T().parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(#1) [Parameter containing:\n",
       "tensor([[-0.9595],\n",
       "        [-0.8490],\n",
       "        [ 0.8159]], requires_grad=True)]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class T(Module):\n",
    "    def __init__(self): self.a = nn.Linear(1, 3, bias=False)\n",
    "\n",
    "t = T()\n",
    "L(t.parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.nn.parameter.Parameter"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(t.a.weight)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_params(size):\n",
    "    return nn.Parameter(torch.zeros(*size).normal_(0, 0.01))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DotProductBias(Module):\n",
    "    def __init__(self, n_users, n_movies, n_factors, y_range=(0,5.5)):\n",
    "        self.user_factors = create_params([n_users, n_factors])\n",
    "        self.user_bias = create_params([n_users])\n",
    "        self.movie_factors = create_params([n_movies, n_factors])\n",
    "        self.movie_bias = create_params([n_movies])\n",
    "        self.y_range = y_range\n",
    "        \n",
    "    def forward(self, x):\n",
    "        users = self.user_factors[x[:,0]]\n",
    "        movies = self.movie_factors[x[:,1]]\n",
    "        res = (users*movies).sum(dim=1)\n",
    "        res += self.user_bias[x[:,0]] + self.movie_bias[x[:,1]]\n",
    "        return sigmoid_range(res, *self.y_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.962146</td>\n",
       "      <td>0.936952</td>\n",
       "      <td>00:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.858084</td>\n",
       "      <td>0.884951</td>\n",
       "      <td>00:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.740883</td>\n",
       "      <td>0.838549</td>\n",
       "      <td>00:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.592497</td>\n",
       "      <td>0.823599</td>\n",
       "      <td>00:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.473570</td>\n",
       "      <td>0.824263</td>\n",
       "      <td>00:14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = DotProductBias(n_users, n_movies, 50)\n",
    "learn = Learner(dls, model, loss_func=MSELossFlat())\n",
    "learn.fit_one_cycle(5, 5e-3, wd=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpreting Embeddings and Biases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Children of the Corn: The Gathering (1996)',\n",
       " 'Lawnmower Man 2: Beyond Cyberspace (1996)',\n",
       " 'Beautician and the Beast, The (1997)',\n",
       " 'Crow: City of Angels, The (1996)',\n",
       " 'Home Alone 3 (1997)']"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_bias = learn.model.movie_bias.squeeze()\n",
    "idxs = movie_bias.argsort()[:5]\n",
    "[dls.classes['title'][i] for i in idxs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['L.A. Confidential (1997)',\n",
       " 'Titanic (1997)',\n",
       " 'Silence of the Lambs, The (1991)',\n",
       " 'Shawshank Redemption, The (1994)',\n",
       " 'Star Wars (1977)']"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idxs = movie_bias.argsort(descending=True)[:5]\n",
    "[dls.classes['title'][i] for i in idxs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = ratings.groupby('title')['rating'].count()\n",
    "top_movies = g.sort_values(ascending=False).index.values[:1000]\n",
    "top_idxs = tensor([learn.dls.classes['title'].o2i[m] for m in top_movies])\n",
    "movie_w = learn.model.movie_factors[top_idxs].cpu().detach()\n",
    "movie_pca = movie_w.pca(3)\n",
    "fac0,fac1,fac2 = movie_pca.t()\n",
    "idxs = np.random.choice(len(top_movies), 50, replace=False)\n",
    "idxs = list(range(50))\n",
    "X = fac0[idxs]\n",
    "Y = fac2[idxs]\n",
    "plt.figure(figsize=(12,12))\n",
    "plt.scatter(X, Y)\n",
    "for i, x, y in zip(top_movies[idxs], X, Y):\n",
    "    plt.text(x,y,i, color=np.random.rand(3)*0.7, fontsize=11)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using fastai.collab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = collab_learner(dls, n_factors=50, y_range=(0, 5.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.931751</td>\n",
       "      <td>0.953806</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.851826</td>\n",
       "      <td>0.878119</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.715254</td>\n",
       "      <td>0.834711</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.583173</td>\n",
       "      <td>0.821470</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.496625</td>\n",
       "      <td>0.821688</td>\n",
       "      <td>00:13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(5, 5e-3, wd=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EmbeddingDotBias(\n",
       "  (u_weight): Embedding(944, 50)\n",
       "  (i_weight): Embedding(1635, 50)\n",
       "  (u_bias): Embedding(944, 1)\n",
       "  (i_bias): Embedding(1635, 1)\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "learn.model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Titanic (1997)',\n",
       " \"Schindler's List (1993)\",\n",
       " 'Shawshank Redemption, The (1994)',\n",
       " 'L.A. Confidential (1997)',\n",
       " 'Silence of the Lambs, The (1991)']"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_bias = learn.model.i_bias.weight.squeeze()\n",
    "idxs = movie_bias.argsort(descending=True)[:5]\n",
    "[dls.classes['title'][i] for i in idxs]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Embedding Distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Dial M for Murder (1954)'"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_factors = learn.model.i_weight.weight\n",
    "idx = dls.classes['title'].o2i['Silence of the Lambs, The (1991)']\n",
    "distances = nn.CosineSimilarity(dim=1)(movie_factors, movie_factors[idx][None])\n",
    "idx = distances.argsort(descending=True)[1]\n",
    "dls.classes['title'][idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bootstrapping a Collaborative Filtering Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deep Learning for Collaborative Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(944, 74), (1635, 101)]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embs = get_emb_sz(dls)\n",
    "embs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CollabNN(Module):\n",
    "    def __init__(self, user_sz, item_sz, y_range=(0,5.5), n_act=100):\n",
    "        self.user_factors = Embedding(*user_sz)\n",
    "        self.item_factors = Embedding(*item_sz)\n",
    "        self.layers = nn.Sequential(\n",
    "            nn.Linear(user_sz[1]+item_sz[1], n_act),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(n_act, 1))\n",
    "        self.y_range = y_range\n",
    "        \n",
    "    def forward(self, x):\n",
    "        embs = self.user_factors(x[:,0]),self.item_factors(x[:,1])\n",
    "        x = self.layers(torch.cat(embs, dim=1))\n",
    "        return sigmoid_range(x, *self.y_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = CollabNN(*embs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.940104</td>\n",
       "      <td>0.959786</td>\n",
       "      <td>00:15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.893943</td>\n",
       "      <td>0.905222</td>\n",
       "      <td>00:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.865591</td>\n",
       "      <td>0.875238</td>\n",
       "      <td>00:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.800177</td>\n",
       "      <td>0.867468</td>\n",
       "      <td>00:14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.760255</td>\n",
       "      <td>0.867455</td>\n",
       "      <td>00:14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = Learner(dls, model, loss_func=MSELossFlat())\n",
    "learn.fit_one_cycle(5, 5e-3, wd=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.002747</td>\n",
       "      <td>0.972392</td>\n",
       "      <td>00:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.926903</td>\n",
       "      <td>0.922348</td>\n",
       "      <td>00:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.877160</td>\n",
       "      <td>0.893401</td>\n",
       "      <td>00:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.838334</td>\n",
       "      <td>0.865040</td>\n",
       "      <td>00:16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.781666</td>\n",
       "      <td>0.864936</td>\n",
       "      <td>00:16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = collab_learner(dls, use_nn=True, y_range=(0, 5.5), layers=[100,50])\n",
    "learn.fit_one_cycle(5, 5e-3, wd=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "@delegates(TabularModel)\n",
    "class EmbeddingNN(TabularModel):\n",
    "    def __init__(self, emb_szs, layers, **kwargs):\n",
    "        super().__init__(emb_szs, layers=layers, n_cont=0, out_sz=1, **kwargs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sidebar: kwargs and Delegates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### End sidebar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Questionnaire"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. What problem does collaborative filtering solve?\n",
    "1. How does it solve it?\n",
    "1. Why might a collaborative filtering predictive model fail to be a very useful recommendation system?\n",
    "1. What does a crosstab representation of collaborative filtering data look like?\n",
    "1. Write the code to create a crosstab representation of the MovieLens data (you might need to do some web searching!).\n",
    "1. What is a latent factor? Why is it \"latent\"?\n",
    "1. What is a dot product? Calculate a dot product manually using pure Python with lists.\n",
    "1. What does `pandas.DataFrame.merge` do?\n",
    "1. What is an embedding matrix?\n",
    "1. What is the relationship between an embedding and a matrix of one-hot-encoded vectors?\n",
    "1. Why do we need `Embedding` if we could use one-hot-encoded vectors for the same thing?\n",
    "1. What does an embedding contain before we start training (assuming we're not using a pretained model)?\n",
    "1. Create a class (without peeking, if possible!) and use it.\n",
    "1. What does `x[:,0]` return?\n",
    "1. Rewrite the `DotProduct` class (without peeking, if possible!) and train a model with it.\n",
    "1. What is a good loss function to use for MovieLens? Why? \n",
    "1. What would happen if we used cross-entropy loss with MovieLens? How would we need to change the model?\n",
    "1. What is the use of bias in a dot product model?\n",
    "1. What is another name for weight decay?\n",
    "1. Write the equation for weight decay (without peeking!).\n",
    "1. Write the equation for the gradient of weight decay. Why does it help reduce weights?\n",
    "1. Why does reducing weights lead to better generalization?\n",
    "1. What does `argsort` do in PyTorch?\n",
    "1. Does sorting the movie biases give the same result as averaging overall movie ratings by movie? Why/why not?\n",
    "1. How do you print the names and details of the layers in a model?\n",
    "1. What is the \"bootstrapping problem\" in collaborative filtering?\n",
    "1. How could you deal with the bootstrapping problem for new users? For new movies?\n",
    "1. How can feedback loops impact collaborative filtering systems?\n",
    "1. When using a neural network in collaborative filtering, why can we have different numbers of factors for movies and users?\n",
    "1. Why is there an `nn.Sequential` in the `CollabNN` model?\n",
    "1. What kind of model should we use if we want to add metadata about users and items, or information such as date and time, to a collaborative filtering model?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Further Research\n",
    "\n",
    "1. Take a look at all the differences between the `Embedding` version of `DotProductBias` and the `create_params` version, and try to understand why each of those changes is required. If you're not sure, try reverting each change to see what happens. (NB: even the type of brackets used in `forward` has changed!)\n",
    "1. Find three other areas where collaborative filtering is being used, and find out what the pros and cons of this approach are in those areas.\n",
    "1. Complete this notebook using the full MovieLens dataset, and compare your results to online benchmarks. See if you can improve your accuracy. Look on the book's website and the fast.ai forum for ideas. Note that there are more columns in the full dataset—see if you can use those too (the next chapter might give you ideas).\n",
    "1. Create a model for MovieLens that works with cross-entropy loss, and compare it to the model in this chapter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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